This paper aims to assess the effects of money laundering (ML) and corruption on socioeconomic development in Iran as a case study for developing countries. We also investigate the complex interactions among socioeconomic development, ML, corruption, private expenditure, and dependence of the Iranian economy on oil production. The paper uses the partial least squares approach to structural equation modeling (PLS-SEM) to estimate an index of Iranian ML over the period 1997– 2017. The results show that in the short run, there is a positive overall effect of ML on economic development. This effect is due to the multiplicative effect of the reinvestment of criminal proceeds in the national economy. We estimate that, in the last decade, a plausible estimate of Iranian ML is approximately 10% of official GDP. From a methodological perspective, this research applies a new calibration approach to convert the latent scores calculated by PLS-SEM into actual values of ML.

Money laundering, corruption and socioeconomic development in Iran: an analysis by structural equation modeling

Dell’Anno, Roberto
;
2023-01-01

Abstract

This paper aims to assess the effects of money laundering (ML) and corruption on socioeconomic development in Iran as a case study for developing countries. We also investigate the complex interactions among socioeconomic development, ML, corruption, private expenditure, and dependence of the Iranian economy on oil production. The paper uses the partial least squares approach to structural equation modeling (PLS-SEM) to estimate an index of Iranian ML over the period 1997– 2017. The results show that in the short run, there is a positive overall effect of ML on economic development. This effect is due to the multiplicative effect of the reinvestment of criminal proceeds in the national economy. We estimate that, in the last decade, a plausible estimate of Iranian ML is approximately 10% of official GDP. From a methodological perspective, this research applies a new calibration approach to convert the latent scores calculated by PLS-SEM into actual values of ML.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4831691
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